The effect of programming on primary school students’ mathematical and scientific understanding: educational use of mBot
This study highlights the importance of an educational design that includes robotics and programming through a visual programming language as a means to enable students to improve substantially their understanding of the elements of logic and mathematics. Gaining an understanding of computational concepts as well as a high degree of student participation and commitment emphasize the effectiveness of introducing robotics and visual programming based on active methodologies in primary education. Implementation of this design provides sixth-grade elementary education students with activities that integrate programming and robotics in sciences and mathematics; these practices allow students to understand coding, motion, engines, sequences and conditionals. A quasi-experimental design, descriptive analysis and participant observation were applied across various dimensions to 93 sixth-grade students in four primary education schools. Programming and robotics were integrated in one didactic unit of mathematics and another in sciences. Statistically significant improvements were achieved in the understanding of mathematical concepts and in the acquisition of computational concepts, based on an active pedagogical practice that instills motivation, enthusiasm, commitment, fun and interest in the content studied.
KeywordsComputational thinking Elementary education Programming and programming languages Robotics Teaching/learning strategies
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Ausubel, D. (1978). In defense of advance organizers: A reply to the critics. Review of Educational Research, 48, 251–257.Google Scholar
- Barak, M., & Zadok, Y. (2009). Robotics projects and learning concepts in science, technology and problem solving. International Journal of Technology and Design Education, 19(3), 289–307.Google Scholar
- Calder, N. (2010). Using scratch: An integrated problem-solving approach to mathematical thinking. Australian Primary Mathematics Classroom, 15(4), 9–14.Google Scholar
- Clark, J., Rogers, M. P., Spradling, C., & Pais, J. (2013). What, no canoes? Lessons learned while hosting a scratch summer camp. Journal of Computing Sciences in Colleges, 28, 204–210.Google Scholar
- Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education. London: Routledge Falmer.Google Scholar
- Fagin, B., & Merkle, L. (2003). Measuring the effectiveness of robots in teaching computer science. In SIGCSE ‘03 proceedings of the 34th SIGCSE technical symposium on computer science education, ACM SIGCSE Bulletin (Vol. 35(1)).Google Scholar
- Freeman, A., Adams Becker, S., Cummins, M., Davis, A., & Hall Giesinger, C. (2017). NMC/CoSN horizon report: 2017 K-12 Edition. Austin, TX: The New Media Consortium. Retrieved from https://www.epiphanymgmt.com/Downloads/horizon%20report.pdf.Google Scholar
- Goetz, J. P., & LeCompte, M. D. (1988). Ethnography and qualitative design in educational research. Madrid: Ediciones Morata.Google Scholar
- Guba, E. G., & Lincoln, Y. S. (1981). Effective evaluation. San Francisco: Jossey-Bass.Google Scholar
- Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
- Han, B., Bae, Y., & Park, J. (2016). The effect of mathematics achievement variables on scratch programming activities of elementary school students. International Journal of Software Engineering and Its Applications, 10(12), 21–30.Google Scholar
- International Society for Technology in Education and the Computer Science Teachers Association. (2011). Operational definition of computational thinking for K-12. http://csta.acm.org/Curriculum/sub/CurrFiles/CompThinkingFlyer.pdf.
- Ishii, N., Suzuki, Y., Fujiyoshi, H., Fujii, T., & Kozawa, M. (2007). A framework for designing and improving learning environments fostering creativity. Psicologia Escolar e Educacional, 11, 59–69.Google Scholar
- Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC horizon report: 2014 K-12 edition. Austin, TX: The New Media Consortium. http://www.nmc.org/pdf/2014-nmc-horizon-report-he-EN.pdf.
- Jonassen, D. H. (1977). Approaches to the study of visual literacy: A brief survey for media personnel. Pennsylvania Media Review, 11, 15–18.Google Scholar
- Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming. Cambridge, MA: MIT Press.Google Scholar
- Kanda, T., Hirano, T., Eaton, D., & Ishiguro, H. (2004). Interactive robots as social partners and peer tutors for children: A field trial. Journal of Human Computer Interaction, 19, 61–84.Google Scholar
- Lambert, L., & Guiffre, H. (2009). Computer science outreach in an elementary school. Journal of Computing Sciences in Colleges, 24(3), 118–124.Google Scholar
- Lin, J. M. C., Yen, L. Y., Yang, M. C., & Chen, C. F. (2005). Teaching computer programming in elementary schools: A pilot study. In National educational computing conference. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.3706&rep=rep1&type=pdf.
- Maxcy, S. J. (2003). Pragmatic threads in mixed methods research in the social sciences: The search for multiple modes of inquiry and the end of the philosophy of formalism. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 51–89). Thousand Oaks, CA: Sage.Google Scholar
- Mazzoni, E., & Benvenuti, M. (2015). A robot-partner for preschool children learning english using socio-cognitive conflict. Educational Technology & Society, 18(4), 474–485.Google Scholar
- Mergendoller, J. R., Maxwell, N. L., & Bellisimo, Y. (2006). The effectiveness of problem-based instruction: A comparative study of instructional methods and student characteristics. The Interdisciplinary Journal of Problem-Based Learning, 1(2), 49–69. https://doi.org/10.7771/1541-5015.1026.Google Scholar
- Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.Google Scholar
- Parmaxi, A., & Zaphiris, P. (2014). The evolvement of constructionism: An overview of the literature. In International conference on learning and collaboration technologies (pp. 452–461). Springer International Publishing. https://doi.org/10.1007/978-3-319-07482-5_43.
- Rogers, C., & Portsmore, M. (2004). Bringing engineering to elementary school. Journal of STEM Education, 5, 17–28.Google Scholar
- Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18, 351–380. https://doi.org/10.1007/s10639-012-9240-x.Google Scholar
- Skelton, G., Pang, Q., Yin, J., Williams, B. J., & Zheng, W. (2010). Introducing engineering concepts to public school students and teachers: Peer-based learning through robotics summer camp. Review of Higher Education and Self-Learning, 3, 1–7.Google Scholar
- Vygotsky, L. S. (1978). Chapter 6: Interaction between learning and development. In M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
- Weng-yi Cheng, R., Shui-fong, L., & Chung-yan Chan, J. (2008). When high achievers and low achievers work in the same group: The roles of group heterogeneity and processes in project-based learning. British Journal of Educational Psychology, 78, 205–221. https://doi.org/10.1348/000709907X218160.Google Scholar